1. Field of the Invention
The present invention relates to the art of production optimization and, more particularly, to a method of optimizing queue times in a production cycle.
2. Description of the Background
Semiconductor wafer fabrication includes a series of carefully designed process steps running on sophisticated capital equipment. The process steps are run in a strictly defined sequence. In many cases, product quality is affected by a total queue time spent on specific process steps, wherein the total queue time includes waiting time, i.e., the time between process steps, dwell time, i.e., the time waiting for a process step to commence and process time i.e., the time spent in the process step. Process steps may include masking, photolithography, etching, rinsing, etc. Thus, for a given process step, two questions are often asked: does queue time have a significant effect on product quality? If yes, what is the time window during which products can be safely processed at this process step?
Conventionally, process steps have been evaluated manually. More specifically, process steps known or suspected to have an input on dependent variables, e.g., yield, quality etc., were chosen, and queue times for the chosen process steps calculated. At that point, a scatter plot was generated to determine whether queue time is correlated to the dependent variable. Unfortunately, various drawbacks exist with the manual process. As process steps are chosen based on experience or theories which may vary with each user, inexperienced users often times do not know which process steps to analyze. Experienced users often times miss new signals associated with new process steps. In addition, as the analysis is performed manually, a considerable amount of time is required to properly analyze a given process step, let alone the numerous process steps associated with a semiconductor wafer fabrication process. Finally, without reliable statistical analysis, any results obtained are highly subjective.
In addition to manual analysis, computer implemented methods are also employed. The computer implemented methods require retrieving manufacturing information associated with a fabrication process, where manufacturing information includes multiple process step pairs. The process step pairs are divided into a high group and a low group through a statistical clustering method. Values, such as p-values, are then calculated for each process step pair. The process step pairs are then ranked and analyzed to identify a particular process step pair. While effective to a degree, the above described method fails to account for individual process steps and different queue time combinations across different combinations of process steps that may have an effect on output. The above described method also fails to evaluate the effect of queue time to yield or performance quantitatively, such as whether a one hour reduction in queue time could increase yield.